Abstract

Exercise tests have played a prominent role in the evaluation of therapies currently used for the management of patients with angina, such as nitrates, b-blockers, and calcium antagonists. Such evaluations have shown dramatic improvements in exercise tolerance, most commonly measured by the time spent exercising until the occurrence of anginal pain or ECG signs of ischaemia, and often amongst patients with severe disease. However, the statistical methods used have generally been based on Normal theory, such as the t-test, or non-parametric equivalents, such as the Wilcoxon rank sum test. Such methods make no allowance for the fact that ischaemic endpoints may not occur in all patients, particularly when patients are under active treatment or in patients with less severe symptoms. In the current situation, where there are several therapeutic options of proven clinical effectiveness, new treatments must be evaluated in opposition, or in addition to existing therapies. Thus it is of particular importance that the statistician responsible for an analysis of exercise test data should use appropriate and efficient techniques, since the benefits of new treatments may be small.

Since exercise times may be censored, in that the event of interest need not occur, it has been recognised that methods for the analysis of survival data are the most appropriate for analyses of exercise test data. Using data from the TIBET Study, a large clinical trial of two anti-anginal therapies administered singly or in combination, this thesis examines in detail the appropriateness of the Cox proportional hazards model, the most popular method for survival regression in the medical literature, to this type of data. It then considers alternatives to this model, and addresses the implications of some common features of exercise test data, in particular the presence of interval censoring and the possibility of multiple exercise tests being conducted on the same patient, using data from the TIBET Study and through simulation studies. Finally, using real data examples, two methods that appear to have received little or not attention with respect to exercise test data are explored, namely competing risks and repeated measures analyses.